Head of Applied AI & Data Science

What matters most

• Govern enterprise AI technical strategy

  The role designs standards and scalable advanced-analytics and AI capabilities that support Ipsen’s Business Data & AI Strategy, while contributing to the broader enterprise AI strategy.

• Own the product lifecycle

  The role oversees internal data and AI products from feasibility and MVP through integration, production deployment, monitoring and maintenance, with accountability for quality, reliability and performance.

• Assess and adopt third-party AI

  The role evaluates external solutions for technical performance, security, regulatory compliance, ethics and strategic fit, then supports adoption, including enterprise productivity solutions.

• Connect business value to measurable outcomes

  The role partners with business domains to identify opportunities, defines performance and impact metrics, and reports progress and outcomes to senior leaders.

• Lead the AI community and delivery partners

  The role leads the Data Science & AI Center of Excellence, mentors data scientists and engineers, and coordinates with business, delivery, data and platform teams while allocating resources.

Likely challenges to think through

• Moving beyond an MVP without losing control

  Likely preparation challenge: rapid experimentation may compete with the reliability and monitoring needed for production. Prepare an example of how you would set evidence-based checkpoints for scaling, integration and ongoing maintenance; this is a trade-off inferred from the stated lifecycle duties, not a reported Ipsen problem.

• Choosing between internal and external solutions

  Likely preparation challenge: a third-party tool may accelerate adoption but require scrutiny of security, privacy, regulatory fit and long-term suitability. Prepare a decision framework that compares those factors with internal development and identifies when Legal, Data Privacy, Quality and Security should be involved.

• Aligning a matrix around limited resources

  Likely preparation challenge: business opportunities may outnumber the capacity of the teams needed to deliver them. Prepare a way to prioritize by expected impact and feasibility, agree on responsibilities across the named functions, and communicate trade-offs to senior stakeholders. This is an inference from the role’s partnership, resource-allocation and reporting duties.

Topics to prepare

• Enterprise AI technical strategy

  The posting assigns this role responsibility for scalable capabilities, standards and technical strategy aligned with business needs. For revision, sketch how you would prioritize two hypothetical AI opportunities, define shared standards and decide what should remain domain-specific.

• AI product lifecycle and operating quality

  The role oversees feasibility, MVP, scaling, integration, production monitoring and maintenance. Rehearse the decision criteria for moving a hypothetical product between those stages, including reliability, performance and ownership.

• Pharmaceutical AI governance

  The posting emphasizes regulatory compliance, privacy, security and ethics, naming the AI Act and collaboration with Legal, Data Privacy, Quality and Security. Revise the questions you would bring to those teams when assessing an internal model or third-party solution; do not assume a particular use case or regulatory classification.

• Model and solution selection

  Required expertise spans machine learning, deep learning, NLP, generative AI, agentic AI and LLMs. Compare when a conventional model, an LLM-based approach or an agentic approach would be justified, and what evidence would establish quality and risk for each.

• Center of Excellence leadership and impact

  The role leads a Data Science & AI Center of Excellence, coaches several engineering disciplines and reports initiative outcomes to senior leaders. Draft a concise explanation of how shared practices, resource allocation and impact metrics could support business value in a matrix organization.

Skills to revise

• Evaluate a third-party AI offering

  Build a sample evaluation scorecard covering technical performance, reliability, security, privacy, regulatory and ethical considerations, strategic fit and adoption. This practices the posting’s explicit vendor-assessment and procurement responsibilities.

• Review an ML deployment workflow

  Using a small synthetic example, outline or rehearse model development with Scikit-learn or PyTorch, experiment tracking with MLflow, and checks for deployment and monitoring. These frameworks and production responsibilities are explicitly listed.

• Trace a cloud data-to-AI architecture

  Draw a hypothetical data flow using only named technologies you know, such as SQL, Python, dbt, Databricks and the listed Azure or AWS services. Mark data-management controls, model integration points and operational monitoring; the exercise prepares for the stated architecture and platform competencies, not a claimed Ipsen architecture.

• Translate business needs into delivery work

  Practice turning a hypothetical stakeholder request into a feasibility question, MVP scope, success metric and Agile delivery outline in Jira or Confluence. The posting calls for business partnership, SDLC experience, those collaboration tools and outcome reporting.

• Present an executive decision

  Prepare a two-minute recommendation for scaling, revising or stopping a hypothetical AI initiative, stating evidence, risks, resource implications and measurable impact in plain language. This rehearses the role’s senior-stakeholder communication and accountability for delivery.

• Map the stated qualifications to evidence

  Prepare a one-page inventory of your degree, years in AI or data science, leadership experience, and relevant delivery and compliance work. The posting specifies an advanced degree, at least 10 years in the field, and at least five years in leadership; pharmaceutical or healthcare team leadership is preferred.

• Draft an enterprise AI strategy outline

  Prepare a short proposal for prioritizing business opportunities, setting technical standards, and governing scalable capabilities. Treat it as a discussion exercise, not a claim about Ipsen’s current strategy.

• Build an end-to-end product walkthrough

  Select a real example you can discuss from feasibility and MVP through integration, production monitoring, and maintenance. Note your decisions, collaborators, quality controls, and outcomes; the role oversees this full lifecycle.

• Sketch a production architecture

  Prepare a diagram showing data flows, model development, deployment, monitoring, and integration with existing systems. Be ready to explain trade-offs using relevant technologies from the posting, such as MLflow, Databricks, Azure, or AWS, only where you have experience.

• Create a third-party AI evaluation rubric

  Draft criteria for technical performance, security, privacy, regulatory compliance, ethics, strategic fit, and adoption. Include where Legal, Data Privacy, Quality, and Security would need to contribute; Ipsen names these partners and the AI Act.

• Prepare a Center of Excellence leadership example

  Outline a real instance of coaching specialists, sharing practices, allocating resources, or aligning teams in a matrix organization. Connect the example to the posted responsibility to lead the Data Science & AI Center of Excellence.

• Turn impact into a leadership briefing

  Prepare a brief account of how you defined success metrics, tracked product performance and business impact, and reported results or setbacks to senior stakeholders. Distinguish measured outcomes from estimates.

• Write questions about scope and decision-making

  Bring questions about how the six Global Data & AI functions collaborate, which technical standards this role owns, how opportunities are prioritized, and how internal products and vendor solutions are evaluated.